This revised edition presents the relevant aspects of transformational geometry, matrix algebra, and calculus to those who may be lacking the necessary mathematical foundations of applied multivariate analysis. It brings up-to-date many definitions of mathematical concepts and their operations. It also clearly defines the relevance of the exercises to concerns within the business community and the social and behavioral sciences. Readers gain a technical background for tackling applications-oriented multivariate texts and receive a geometric perspective for understanding multivariate methods."Mathematical Tools for Applied Multivariate Analysis, Revised Edition illustrates major concepts in matrix algebra, linear structures, and eigenstructures geometrically, numerically, and algebraically. The authors emphasize the applications of these techniques by discussing potential solutions to problems outlined early in the book. They also present small numerical examples of the various concepts.
Rezensionen / Stimmen
"This revision includes an update of terminology and basic mathematical concepts necessitated by the increasing use of multivariate techniques in a wide range of applied fields. It is highly recommended as a companion text for courses in multivariate methods and theory." --JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
"[The book's] approach is unique and should be an interesting and effective way to learn basic linear algebra, even for some who are primarily interested in linear algebra for its own sake." --CHOICE
"It provides a careful and thorough introduction to vectors and matrices. Especially valuable is the material providing geometric interpretations...A particular strength of the book is the frequent use of small numerical examples which, for example, actually demonstrate the useful properties of determinants, and make absolutely clear what is meant by operations like the multiplication of matrices. The book is designed for readers who have no prior knowledge of matrix theory, and specifically for students in the behavioural and administrative sciences. However, it is also very clear and useful that it has material of value to anyone using multivariate methods. It should be on the reading list for all courses on multivariate analysis." --B.J.T. Morgan, Univeristy of Kent, Canterbury, U.K. in SHORT BOOK REVIEWS, December 1998
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science Publishing Co Inc
Zielgruppe
Für Beruf und Forschung
Undergraduate and graduate-level courses in quantitative methods and applied multivariate analysis. These courses include: applied multivariate analysis in statistics departments, introductory applied statistics and statisticaltechniques in psychology departments, sociological research in sociology departments, social statistics and marketing information in marketing departments, and mathematics for economists in economics departments.
Maße
Höhe: 229 mm
Breite: 152 mm
Gewicht
ISBN-13
978-0-12-160955-9 (9780121609559)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
J. Douglas Carroll is the Board of Governor's Professor of Marketing and Psychology in the Graduate School of Management at Rutgers, the State University of New Jersey. He holds a Ph.D. in mathematics from Princeton University. Dr. Carroll has published widely on multidimensional scaling and related techniques of data analysis. He is a member of several professional organizations.
Autor*in
Rutgers University, New Brunswick, New Jersey, U.S.A.
La Jolla Institute for Allergy and Immunology, La Jolla, California, U.S.A.
Herausgeber*in
AT&T Bell Labs, Murray Hill, New Jersey
The Nature of Multivariate Data Analysis.
Vector and Matrix Operations for Multivariate Analysis.
Vector and Matrix Concepts from a Geometric Viewpoint.
Linear Transformations from a Geometric Viewpoint.
Decomposition of Matrix Transformations: Eigenstructures and Quadratic Forms.
Applying the Tools to Multivariate Data.
Appendix A: Symbolic Differentiation and Optimization of Multivariable Functions.
Appendix B: Linear Equations and Generalized Inverses.
Answers to Numerical Problems.
References.
Index.
